Overview

Dataset statistics

Number of variables32
Number of observations119390
Missing cells129425
Missing cells (%)3.4%
Duplicate rows31994
Duplicate rows (%)26.8%
Total size in memory23.7 MiB
Average record size in memory208.0 B

Variable types

NUM17
CAT13
BOOL2

Warnings

Dataset has 31994 (26.8%) duplicate rows Duplicates
country has a high cardinality: 177 distinct values High cardinality
reservation_status_date has a high cardinality: 926 distinct values High cardinality
agent has 16340 (13.7%) missing values Missing
company has 112593 (94.3%) missing values Missing
babies is highly skewed (γ1 = 24.64654483) Skewed
previous_cancellations is highly skewed (γ1 = 24.45804872) Skewed
previous_bookings_not_canceled is highly skewed (γ1 = 23.53979995) Skewed
lead_time has 6345 (5.3%) zeros Zeros
stays_in_weekend_nights has 51998 (43.6%) zeros Zeros
stays_in_week_nights has 7645 (6.4%) zeros Zeros
children has 110796 (92.8%) zeros Zeros
babies has 118473 (99.2%) zeros Zeros
previous_cancellations has 112906 (94.6%) zeros Zeros
previous_bookings_not_canceled has 115770 (97.0%) zeros Zeros
booking_changes has 101314 (84.9%) zeros Zeros
days_in_waiting_list has 115692 (96.9%) zeros Zeros
adr has 1959 (1.6%) zeros Zeros
required_car_parking_spaces has 111974 (93.8%) zeros Zeros
total_of_special_requests has 70318 (58.9%) zeros Zeros

Reproduction

Analysis started2020-10-08 20:59:01.100781
Analysis finished2020-10-08 21:01:06.145076
Duration2 minutes and 5.04 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

hotel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size466.4 KiB
City Hotel
79330 
Resort Hotel
40060 
ValueCountFrequency (%) 
City Hotel7933066.4%
 
Resort Hotel4006033.6%
 
2020-10-08T23:01:06.554497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T23:01:06.803276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:01:07.366425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length12
Median length10
Mean length10.67107798
Min length10
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.7 KiB
0
75166 
1
44224 
ValueCountFrequency (%) 
07516663.0%
 
14422437.0%
 
2020-10-08T23:01:07.964460image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

lead_time
Real number (ℝ≥0)

ZEROS

Distinct479
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.0114164
Minimum0
Maximum737
Zeros6345
Zeros (%)5.3%
Memory size932.7 KiB
2020-10-08T23:01:08.586083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median69
Q3160
95-th percentile320
Maximum737
Range737
Interquartile range (IQR)142

Descriptive statistics

Standard deviation106.863097
Coefficient of variation (CV)1.027416997
Kurtosis1.696448849
Mean104.0114164
Median Absolute Deviation (MAD)60
Skewness1.346549873
Sum12417923
Variance11419.72151
MonotocityNot monotonic
2020-10-08T23:01:09.046572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
063455.3%
 
134602.9%
 
220691.7%
 
318161.5%
 
417151.4%
 
515651.3%
 
614451.2%
 
713311.1%
 
811381.0%
 
1210790.9%
 
Other values (469)9742781.6%
 
ValueCountFrequency (%) 
063455.3%
 
134602.9%
 
220691.7%
 
318161.5%
 
417151.4%
 
ValueCountFrequency (%) 
7371< 0.1%
 
7091< 0.1%
 
62917< 0.1%
 
62630< 0.1%
 
62217< 0.1%
 
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.7 KiB
2016
56707 
2017
40687 
2015
21996 
ValueCountFrequency (%) 
20165670747.5%
 
20174068734.1%
 
20152199618.4%
 
2020-10-08T23:01:09.868829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T23:01:09.987318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:01:10.141724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length4
Median length4
Mean length4
Min length4
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size466.4 KiB
August
13877 
July
12661 
May
11791 
October
11160 
April
11089 
Other values (7)
58812 
ValueCountFrequency (%) 
August1387711.6%
 
July1266110.6%
 
May117919.9%
 
October111609.3%
 
April110899.3%
 
June109399.2%
 
September105088.8%
 
March97948.2%
 
February80686.8%
 
November67945.7%
 
Other values (2)1270910.6%
 
2020-10-08T23:01:10.582050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T23:01:10.814145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length6
Mean length5.903182846
Min length3

arrival_date_week_number
Real number (ℝ≥0)

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.16517296
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Memory size932.7 KiB
2020-10-08T23:01:11.002220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q116
median28
Q338
95-th percentile49
Maximum53
Range52
Interquartile range (IQR)22

Descriptive statistics

Standard deviation13.60513836
Coefficient of variation (CV)0.500830176
Kurtosis-0.9860771763
Mean27.16517296
Median Absolute Deviation (MAD)11
Skewness-0.01001432604
Sum3243250
Variance185.0997897
MonotocityNot monotonic
2020-10-08T23:01:11.200434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3335803.0%
 
3030872.6%
 
3230452.6%
 
3430402.5%
 
1829262.5%
 
2128542.4%
 
2828532.4%
 
1728052.3%
 
2027852.3%
 
2927632.3%
 
Other values (43)8965275.1%
 
ValueCountFrequency (%) 
110470.9%
 
212181.0%
 
313191.1%
 
414871.2%
 
513871.2%
 
ValueCountFrequency (%) 
5318161.5%
 
5211951.0%
 
519330.8%
 
5015051.3%
 
4917821.5%
 

arrival_date_day_of_month
Real number (ℝ≥0)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.79824106
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size932.7 KiB
2020-10-08T23:01:11.382110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.780829471
Coefficient of variation (CV)0.5558105765
Kurtosis-1.187168319
Mean15.79824106
Median Absolute Deviation (MAD)8
Skewness-0.002000453979
Sum1886152
Variance77.10296619
MonotocityNot monotonic
2020-10-08T23:01:11.522739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%) 
1744063.7%
 
543173.6%
 
1541963.5%
 
2541603.5%
 
2641473.5%
 
940963.4%
 
1240873.4%
 
1640783.4%
 
240553.4%
 
1940523.4%
 
Other values (21)7779665.2%
 
ValueCountFrequency (%) 
136263.0%
 
240553.4%
 
338553.2%
 
437633.2%
 
543173.6%
 
ValueCountFrequency (%) 
3122081.8%
 
3038533.2%
 
2935803.0%
 
2839463.3%
 
2738023.2%
 

stays_in_weekend_nights
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9275986264
Minimum0
Maximum19
Zeros51998
Zeros (%)43.6%
Memory size932.7 KiB
2020-10-08T23:01:11.684563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.9986134946
Coefficient of variation (CV)1.076557755
Kurtosis7.174066064
Mean0.9275986264
Median Absolute Deviation (MAD)1
Skewness1.38004645
Sum110746
Variance0.9972289116
MonotocityNot monotonic
2020-10-08T23:01:11.822722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
05199843.6%
 
23330827.9%
 
13062625.7%
 
418551.6%
 
312591.1%
 
61530.1%
 
5790.1%
 
8600.1%
 
719< 0.1%
 
911< 0.1%
 
Other values (7)22< 0.1%
 
ValueCountFrequency (%) 
05199843.6%
 
13062625.7%
 
23330827.9%
 
312591.1%
 
418551.6%
 
ValueCountFrequency (%) 
191< 0.1%
 
181< 0.1%
 
163< 0.1%
 
142< 0.1%
 
133< 0.1%
 

stays_in_week_nights
Real number (ℝ≥0)

ZEROS

Distinct35
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.500301533
Minimum0
Maximum50
Zeros7645
Zeros (%)6.4%
Memory size932.7 KiB
2020-10-08T23:01:12.003111image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile5
Maximum50
Range50
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.908285615
Coefficient of variation (CV)0.7632221914
Kurtosis24.28455482
Mean2.500301533
Median Absolute Deviation (MAD)1
Skewness2.862249242
Sum298511
Variance3.641553989
MonotocityNot monotonic
2020-10-08T23:01:12.239083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%) 
23368428.2%
 
13031025.4%
 
32225818.6%
 
5110779.3%
 
495638.0%
 
076456.4%
 
614991.3%
 
1010360.9%
 
710290.9%
 
86560.5%
 
Other values (25)6330.5%
 
ValueCountFrequency (%) 
076456.4%
 
13031025.4%
 
23368428.2%
 
32225818.6%
 
495638.0%
 
ValueCountFrequency (%) 
501< 0.1%
 
421< 0.1%
 
411< 0.1%
 
402< 0.1%
 
351< 0.1%
 

adults
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.856403384
Minimum0
Maximum55
Zeros403
Zeros (%)0.3%
Memory size932.7 KiB
2020-10-08T23:01:12.450220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q32
95-th percentile3
Maximum55
Range55
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5792609988
Coefficient of variation (CV)0.3120340137
Kurtosis1352.115116
Mean1.856403384
Median Absolute Deviation (MAD)0
Skewness18.31780476
Sum221636
Variance0.3355433048
MonotocityNot monotonic
2020-10-08T23:01:12.635356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%) 
28968075.1%
 
12302719.3%
 
362025.2%
 
04030.3%
 
4620.1%
 
265< 0.1%
 
272< 0.1%
 
202< 0.1%
 
52< 0.1%
 
551< 0.1%
 
Other values (4)4< 0.1%
 
ValueCountFrequency (%) 
04030.3%
 
12302719.3%
 
28968075.1%
 
362025.2%
 
4620.1%
 
ValueCountFrequency (%) 
551< 0.1%
 
501< 0.1%
 
401< 0.1%
 
272< 0.1%
 
265< 0.1%
 

children
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)< 0.1%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.1038899033
Minimum0
Maximum10
Zeros110796
Zeros (%)92.8%
Memory size932.7 KiB
2020-10-08T23:01:12.814383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.3985614448
Coefficient of variation (CV)3.836382863
Kurtosis18.67369236
Mean0.1038899033
Median Absolute Deviation (MAD)0
Skewness4.112589542
Sum12403
Variance0.1588512253
MonotocityNot monotonic
2020-10-08T23:01:13.112479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
011079692.8%
 
148614.1%
 
236523.1%
 
3760.1%
 
101< 0.1%
 
(Missing)4< 0.1%
 
ValueCountFrequency (%) 
011079692.8%
 
148614.1%
 
236523.1%
 
3760.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
101< 0.1%
 
3760.1%
 
236523.1%
 
148614.1%
 
011079692.8%
 

babies
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.007948739425
Minimum0
Maximum10
Zeros118473
Zeros (%)99.2%
Memory size932.7 KiB
2020-10-08T23:01:13.337062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0974361913
Coefficient of variation (CV)12.25806837
Kurtosis1633.948235
Mean0.007948739425
Median Absolute Deviation (MAD)0
Skewness24.64654483
Sum949
Variance0.009493811375
MonotocityNot monotonic
2020-10-08T23:01:13.602913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
011847399.2%
 
19000.8%
 
215< 0.1%
 
101< 0.1%
 
91< 0.1%
 
ValueCountFrequency (%) 
011847399.2%
 
19000.8%
 
215< 0.1%
 
91< 0.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
101< 0.1%
 
91< 0.1%
 
215< 0.1%
 
19000.8%
 
011847399.2%
 

meal
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size466.4 KiB
BB
92310 
HB
14463 
SC
10650 
Undefined
 
1169
FB
 
798
ValueCountFrequency (%) 
BB9231077.3%
 
HB1446312.1%
 
SC106508.9%
 
Undefined11691.0%
 
FB7980.7%
 
2020-10-08T23:01:13.769306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T23:01:13.979359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:01:14.171228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length2
Mean length2.068540079
Min length2

country
Categorical

HIGH CARDINALITY

Distinct177
Distinct (%)0.1%
Missing488
Missing (%)0.4%
Memory size466.4 KiB
PRT
48590 
GBR
12129 
FRA
10415 
ESP
8568 
DEU
7287 
Other values (172)
31913 
ValueCountFrequency (%) 
PRT4859040.7%
 
GBR1212910.2%
 
FRA104158.7%
 
ESP85687.2%
 
DEU72876.1%
 
ITA37663.2%
 
IRL33752.8%
 
BEL23422.0%
 
BRA22241.9%
 
NLD21041.8%
 
Other values (167)1810215.2%
 
2020-10-08T23:01:14.489316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique30 ?
Unique (%)< 0.1%
2020-10-08T23:01:14.675433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length3
Median length3
Mean length2.98928721
Min length2

market_segment
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size466.4 KiB
Online TA
56477 
Offline TA/TO
24219 
Groups
19811 
Direct
12606 
Corporate
 
5295
Other values (3)
 
982
ValueCountFrequency (%) 
Online TA5647747.3%
 
Offline TA/TO2421920.3%
 
Groups1981116.6%
 
Direct1260610.6%
 
Corporate52954.4%
 
Complementary7430.6%
 
Aviation2370.2%
 
Undefined2< 0.1%
 
2020-10-08T23:01:15.001827image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T23:01:15.137748image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:01:15.763423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length13
Median length9
Mean length9.01976715
Min length6
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size466.4 KiB
TA/TO
97870 
Direct
14645 
Corporate
 
6677
GDS
 
193
Undefined
 
5
ValueCountFrequency (%) 
TA/TO9787082.0%
 
Direct1464512.3%
 
Corporate66775.6%
 
GDS1930.2%
 
Undefined5< 0.1%
 
2020-10-08T23:01:15.958946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T23:01:16.235155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:01:16.588453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length5
Mean length5.343303459
Min length3
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size932.7 KiB
0
115580 
1
 
3810
ValueCountFrequency (%) 
011558096.8%
 
138103.2%
 
2020-10-08T23:01:16.702401image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

previous_cancellations
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08711784907
Minimum0
Maximum26
Zeros112906
Zeros (%)94.6%
Memory size932.7 KiB
2020-10-08T23:01:16.798318image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8443363842
Coefficient of variation (CV)9.691887405
Kurtosis674.0736926
Mean0.08711784907
Median Absolute Deviation (MAD)0
Skewness24.45804872
Sum10401
Variance0.7129039296
MonotocityNot monotonic
2020-10-08T23:01:16.941420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%) 
011290694.6%
 
160515.1%
 
21160.1%
 
3650.1%
 
2448< 0.1%
 
1135< 0.1%
 
431< 0.1%
 
2626< 0.1%
 
2525< 0.1%
 
622< 0.1%
 
Other values (5)650.1%
 
ValueCountFrequency (%) 
011290694.6%
 
160515.1%
 
21160.1%
 
3650.1%
 
431< 0.1%
 
ValueCountFrequency (%) 
2626< 0.1%
 
2525< 0.1%
 
2448< 0.1%
 
211< 0.1%
 
1919< 0.1%
 

previous_bookings_not_canceled
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct73
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1370969093
Minimum0
Maximum72
Zeros115770
Zeros (%)97.0%
Memory size932.7 KiB
2020-10-08T23:01:17.287609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum72
Range72
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.497436848
Coefficient of variation (CV)10.92246977
Kurtosis767.2452097
Mean0.1370969093
Median Absolute Deviation (MAD)0
Skewness23.53979995
Sum16368
Variance2.242317113
MonotocityNot monotonic
2020-10-08T23:01:17.572120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
011577097.0%
 
115421.3%
 
25800.5%
 
33330.3%
 
42290.2%
 
51810.2%
 
61150.1%
 
7880.1%
 
8700.1%
 
9600.1%
 
Other values (63)4220.4%
 
ValueCountFrequency (%) 
011577097.0%
 
115421.3%
 
25800.5%
 
33330.3%
 
42290.2%
 
ValueCountFrequency (%) 
721< 0.1%
 
711< 0.1%
 
701< 0.1%
 
691< 0.1%
 
681< 0.1%
 
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size466.4 KiB
A
85994 
D
19201 
E
 
6535
F
 
2897
G
 
2094
Other values (5)
 
2669
ValueCountFrequency (%) 
A8599472.0%
 
D1920116.1%
 
E65355.5%
 
F28972.4%
 
G20941.8%
 
B11180.9%
 
C9320.8%
 
H6010.5%
 
P12< 0.1%
 
L6< 0.1%
 
2020-10-08T23:01:17.777284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T23:01:17.895842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:01:18.144431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1
Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size466.4 KiB
A
74053 
D
25322 
E
7806 
F
 
3751
G
 
2553
Other values (7)
 
5905
ValueCountFrequency (%) 
A7405362.0%
 
D2532221.2%
 
E78066.5%
 
F37513.1%
 
G25532.1%
 
C23752.0%
 
B21631.8%
 
H7120.6%
 
I3630.3%
 
K2790.2%
 
Other values (2)13< 0.1%
 
2020-10-08T23:01:18.407648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique1 ?
Unique (%)< 0.1%
2020-10-08T23:01:18.584286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length1
Median length1
Mean length1
Min length1

booking_changes
Real number (ℝ≥0)

ZEROS

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2211240472
Minimum0
Maximum21
Zeros101314
Zeros (%)84.9%
Memory size932.7 KiB
2020-10-08T23:01:18.739196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6523055727
Coefficient of variation (CV)2.949953118
Kurtosis79.39360467
Mean0.2211240472
Median Absolute Deviation (MAD)0
Skewness6.000270054
Sum26400
Variance0.4255025601
MonotocityNot monotonic
2020-10-08T23:01:18.877435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
010131484.9%
 
11270110.6%
 
238053.2%
 
39270.8%
 
43760.3%
 
51180.1%
 
6630.1%
 
731< 0.1%
 
817< 0.1%
 
98< 0.1%
 
Other values (11)30< 0.1%
 
ValueCountFrequency (%) 
010131484.9%
 
11270110.6%
 
238053.2%
 
39270.8%
 
43760.3%
 
ValueCountFrequency (%) 
211< 0.1%
 
201< 0.1%
 
181< 0.1%
 
172< 0.1%
 
162< 0.1%
 

deposit_type
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size466.4 KiB
No Deposit
104641 
Non Refund
14587 
Refundable
 
162
ValueCountFrequency (%) 
No Deposit10464187.6%
 
Non Refund1458712.2%
 
Refundable1620.1%
 
2020-10-08T23:01:19.164255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T23:01:19.275064image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:01:19.411317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

agent
Real number (ℝ≥0)

MISSING

Distinct333
Distinct (%)0.3%
Missing16340
Missing (%)13.7%
Infinite0
Infinite (%)0.0%
Mean86.69338185
Minimum1
Maximum535
Zeros0
Zeros (%)0.0%
Memory size932.7 KiB
2020-10-08T23:01:19.561050image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q19
median14
Q3229
95-th percentile250
Maximum535
Range534
Interquartile range (IQR)220

Descriptive statistics

Standard deviation110.7745476
Coefficient of variation (CV)1.277773981
Kurtosis-0.007179564938
Mean86.69338185
Median Absolute Deviation (MAD)13
Skewness1.089385636
Sum8933753
Variance12271.00041
MonotocityNot monotonic
2020-10-08T23:01:19.753708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
93196126.8%
 
2401392211.7%
 
171916.0%
 
1436403.0%
 
735393.0%
 
632902.8%
 
25028702.4%
 
24117211.4%
 
2816661.4%
 
815141.3%
 
Other values (323)3173626.6%
 
(Missing)1634013.7%
 
ValueCountFrequency (%) 
171916.0%
 
21620.1%
 
313361.1%
 
447< 0.1%
 
53300.3%
 
ValueCountFrequency (%) 
5353< 0.1%
 
531680.1%
 
52735< 0.1%
 
52610< 0.1%
 
5102< 0.1%
 

company
Real number (ℝ≥0)

MISSING

Distinct352
Distinct (%)5.2%
Missing112593
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean189.2667353
Minimum6
Maximum543
Zeros0
Zeros (%)0.0%
Memory size932.7 KiB
2020-10-08T23:01:19.935213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile40
Q162
median179
Q3270
95-th percentile435
Maximum543
Range537
Interquartile range (IQR)208

Descriptive statistics

Standard deviation131.6550146
Coefficient of variation (CV)0.6956056721
Kurtosis-0.4907952103
Mean189.2667353
Median Absolute Deviation (MAD)111
Skewness0.6015996673
Sum1286446
Variance17333.04288
MonotocityNot monotonic
2020-10-08T23:01:20.117102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
409270.8%
 
2237840.7%
 
672670.2%
 
452500.2%
 
1532150.2%
 
1741490.1%
 
2191410.1%
 
2811380.1%
 
1541330.1%
 
4051190.1%
 
Other values (342)36743.1%
 
(Missing)11259394.3%
 
ValueCountFrequency (%) 
61< 0.1%
 
81< 0.1%
 
937< 0.1%
 
101< 0.1%
 
111< 0.1%
 
ValueCountFrequency (%) 
5432< 0.1%
 
5411< 0.1%
 
5392< 0.1%
 
5342< 0.1%
 
5311< 0.1%
 

days_in_waiting_list
Real number (ℝ≥0)

ZEROS

Distinct128
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.321149175
Minimum0
Maximum391
Zeros115692
Zeros (%)96.9%
Memory size932.7 KiB
2020-10-08T23:01:20.371185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum391
Range391
Interquartile range (IQR)0

Descriptive statistics

Standard deviation17.59472088
Coefficient of variation (CV)7.580176694
Kurtosis186.7930696
Mean2.321149175
Median Absolute Deviation (MAD)0
Skewness11.94435345
Sum277122
Variance309.5742028
MonotocityNot monotonic
2020-10-08T23:01:20.859296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
011569296.9%
 
392270.2%
 
581640.1%
 
441410.1%
 
311270.1%
 
35960.1%
 
46940.1%
 
69890.1%
 
63830.1%
 
50800.1%
 
Other values (118)25972.2%
 
ValueCountFrequency (%) 
011569296.9%
 
112< 0.1%
 
25< 0.1%
 
359< 0.1%
 
425< 0.1%
 
ValueCountFrequency (%) 
39145< 0.1%
 
37915< 0.1%
 
33015< 0.1%
 
25910< 0.1%
 
23635< 0.1%
 

customer_type
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size466.4 KiB
Transient
89613 
Transient-Party
25124 
Contract
 
4076
Group
 
577
ValueCountFrequency (%) 
Transient8961375.1%
 
Transient-Party2512421.0%
 
Contract40763.4%
 
Group5770.5%
 
2020-10-08T23:01:21.345771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T23:01:21.594426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:01:21.954213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length15
Median length9
Mean length10.20914649
Min length5

adr
Real number (ℝ)

ZEROS

Distinct8879
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101.8311215
Minimum-6.38
Maximum5400
Zeros1959
Zeros (%)1.6%
Memory size932.7 KiB
2020-10-08T23:01:22.466083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-6.38
5-th percentile38.4
Q169.29
median94.575
Q3126
95-th percentile193.5
Maximum5400
Range5406.38
Interquartile range (IQR)56.71

Descriptive statistics

Standard deviation50.53579029
Coefficient of variation (CV)0.4962705853
Kurtosis1013.189851
Mean101.8311215
Median Absolute Deviation (MAD)27.825
Skewness10.53021398
Sum12157617.6
Variance2553.8661
MonotocityNot monotonic
2020-10-08T23:01:23.010983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6237543.1%
 
7527152.3%
 
9024732.1%
 
6524182.0%
 
019591.6%
 
8018891.6%
 
9516611.4%
 
12016071.3%
 
10015731.3%
 
8515381.3%
 
Other values (8869)9780381.9%
 
ValueCountFrequency (%) 
-6.381< 0.1%
 
019591.6%
 
0.261< 0.1%
 
0.51< 0.1%
 
115< 0.1%
 
ValueCountFrequency (%) 
54001< 0.1%
 
5101< 0.1%
 
5081< 0.1%
 
451.51< 0.1%
 
4501< 0.1%
 

required_car_parking_spaces
Real number (ℝ≥0)

ZEROS

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06251779881
Minimum0
Maximum8
Zeros111974
Zeros (%)93.8%
Memory size932.7 KiB
2020-10-08T23:01:23.666417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2452911475
Coefficient of variation (CV)3.92354101
Kurtosis29.99805617
Mean0.06251779881
Median Absolute Deviation (MAD)0
Skewness4.163233238
Sum7464
Variance0.06016774703
MonotocityNot monotonic
2020-10-08T23:01:24.197958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
011197493.8%
 
173836.2%
 
228< 0.1%
 
33< 0.1%
 
82< 0.1%
 
ValueCountFrequency (%) 
011197493.8%
 
173836.2%
 
228< 0.1%
 
33< 0.1%
 
82< 0.1%
 
ValueCountFrequency (%) 
82< 0.1%
 
33< 0.1%
 
228< 0.1%
 
173836.2%
 
011197493.8%
 

total_of_special_requests
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5713627607
Minimum0
Maximum5
Zeros70318
Zeros (%)58.9%
Memory size932.7 KiB
2020-10-08T23:01:24.864367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7927984228
Coefficient of variation (CV)1.387557043
Kurtosis1.492564811
Mean0.5713627607
Median Absolute Deviation (MAD)0
Skewness1.349189377
Sum68215
Variance0.6285293392
MonotocityNot monotonic
2020-10-08T23:01:25.348345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%) 
07031858.9%
 
13322627.8%
 
21296910.9%
 
324972.1%
 
43400.3%
 
540< 0.1%
 
ValueCountFrequency (%) 
07031858.9%
 
13322627.8%
 
21296910.9%
 
324972.1%
 
43400.3%
 
ValueCountFrequency (%) 
540< 0.1%
 
43400.3%
 
324972.1%
 
21296910.9%
 
13322627.8%
 
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size466.4 KiB
Check-Out
75166 
Canceled
43017 
No-Show
 
1207
ValueCountFrequency (%) 
Check-Out7516663.0%
 
Canceled4301736.0%
 
No-Show12071.0%
 
2020-10-08T23:01:25.826025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-08T23:01:26.009623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:01:26.531131image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length9
Median length9
Mean length8.619473993
Min length7

reservation_status_date
Categorical

HIGH CARDINALITY

Distinct926
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size466.4 KiB
2015-10-21
 
1461
2015-07-06
 
805
2016-11-25
 
790
2015-01-01
 
763
2016-01-18
 
625
Other values (921)
114946 
ValueCountFrequency (%) 
2015-10-2114611.2%
 
2015-07-068050.7%
 
2016-11-257900.7%
 
2015-01-017630.6%
 
2016-01-186250.5%
 
2015-07-024690.4%
 
2016-12-074500.4%
 
2015-12-184230.4%
 
2016-02-094120.3%
 
2016-04-043820.3%
 
Other values (916)11281094.5%
 
2020-10-08T23:01:27.028340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique28 ?
Unique (%)< 0.1%
2020-10-08T23:01:27.534234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length10
Median length10
Mean length10
Min length10

Interactions

2020-10-08T22:59:34.362798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:34.770345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:35.082571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:35.714305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:35.996449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:36.375388image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:36.793134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:37.177867image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:37.561811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:38.240140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:38.494671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:38.660783image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:38.840565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:39.110493image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:39.388471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:39.543336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:39.788089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:39.950543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:40.100554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:40.247767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:40.467769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:40.652591image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:40.819752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:41.015758image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:41.258842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:41.474608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:41.693850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:41.905649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:42.075515image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:42.257508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:42.422723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:42.590690image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:42.775763image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:43.009334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:43.164608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:43.327317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:43.492273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:43.658985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:43.901797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:44.060710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:44.239396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:44.428485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:44.609924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:44.950501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:45.128145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:45.297391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:45.460248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T22:59:45.618026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2020-10-08T23:00:41.953815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:42.371236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:42.567372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:42.735288image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:42.886559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:43.079506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:43.503731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:43.878395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:44.034866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:44.363500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:44.536463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:44.779979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:44.979817image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:45.127492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:45.327649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:45.553241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:45.701947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:45.853528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:46.107476image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:46.410407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:46.634227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:46.798155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:46.951175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:47.126547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:47.299360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:47.450402image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:47.658404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:47.820935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:48.016194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:48.166430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:48.667871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:48.980568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:49.456169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:50.099430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-08T23:01:27.938261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-08T23:01:29.152309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-08T23:01:30.135369image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-08T23:01:31.304432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-10-08T23:01:32.554498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-10-08T23:00:51.810566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:00:59.508140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:01:02.699266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-08T23:01:03.817171image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
0Resort Hotel03422015July2710020.00BBPRTDirectDirect000CC3No DepositNaNNaN0Transient0.000Check-Out2015-07-01
1Resort Hotel07372015July2710020.00BBPRTDirectDirect000CC4No DepositNaNNaN0Transient0.000Check-Out2015-07-01
2Resort Hotel072015July2710110.00BBGBRDirectDirect000AC0No DepositNaNNaN0Transient75.000Check-Out2015-07-02
3Resort Hotel0132015July2710110.00BBGBRCorporateCorporate000AA0No Deposit304.0NaN0Transient75.000Check-Out2015-07-02
4Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240.0NaN0Transient98.001Check-Out2015-07-03
5Resort Hotel0142015July2710220.00BBGBROnline TATA/TO000AA0No Deposit240.0NaN0Transient98.001Check-Out2015-07-03
6Resort Hotel002015July2710220.00BBPRTDirectDirect000CC0No DepositNaNNaN0Transient107.000Check-Out2015-07-03
7Resort Hotel092015July2710220.00FBPRTDirectDirect000CC0No Deposit303.0NaN0Transient103.001Check-Out2015-07-03
8Resort Hotel1852015July2710320.00BBPRTOnline TATA/TO000AA0No Deposit240.0NaN0Transient82.001Canceled2015-05-06
9Resort Hotel1752015July2710320.00HBPRTOffline TA/TOTA/TO000DD0No Deposit15.0NaN0Transient105.500Canceled2015-04-22

Last rows

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_date
119380City Hotel0442017August35311320.00SCDEUOnline TATA/TO000AA0No Deposit9.0NaN0Transient140.7501Check-Out2017-09-04
119381City Hotel01882017August35312320.00BBDEUDirectDirect000AA0No Deposit14.0NaN0Transient99.0000Check-Out2017-09-05
119382City Hotel01352017August35302430.00BBJPNOnline TATA/TO000GG0No Deposit7.0NaN0Transient209.0000Check-Out2017-09-05
119383City Hotel01642017August35312420.00BBDEUOffline TA/TOTA/TO000AA0No Deposit42.0NaN0Transient87.6000Check-Out2017-09-06
119384City Hotel0212017August35302520.00BBBELOffline TA/TOTA/TO000AA0No Deposit394.0NaN0Transient96.1402Check-Out2017-09-06
119385City Hotel0232017August35302520.00BBBELOffline TA/TOTA/TO000AA0No Deposit394.0NaN0Transient96.1400Check-Out2017-09-06
119386City Hotel01022017August35312530.00BBFRAOnline TATA/TO000EE0No Deposit9.0NaN0Transient225.4302Check-Out2017-09-07
119387City Hotel0342017August35312520.00BBDEUOnline TATA/TO000DD0No Deposit9.0NaN0Transient157.7104Check-Out2017-09-07
119388City Hotel01092017August35312520.00BBGBROnline TATA/TO000AA0No Deposit89.0NaN0Transient104.4000Check-Out2017-09-07
119389City Hotel02052017August35292720.00HBDEUOnline TATA/TO000AA0No Deposit9.0NaN0Transient151.2002Check-Out2017-09-07

Duplicate rows

Most frequent

hotelis_canceledlead_timearrival_date_yeararrival_date_montharrival_date_week_numberarrival_date_day_of_monthstays_in_weekend_nightsstays_in_week_nightsadultschildrenbabiesmealcountrymarket_segmentdistribution_channelis_repeated_guestprevious_cancellationsprevious_bookings_not_canceledreserved_room_typeassigned_room_typebooking_changesdeposit_typeagentcompanydays_in_waiting_listcustomer_typeadrrequired_car_parking_spacestotal_of_special_requestsreservation_statusreservation_status_datecount
1City Hotel02562016October43162320.00BBDEUOnline TATA/TO000AA0No Deposit9.0333.00Transient-Party100.7500Check-Out2016-10-217
4Resort Hotel0242015November45331010.00BBFRACorporateCorporate000AA2No Deposit334.0281.00Transient-Party40.0000Check-Out2015-11-165
14Resort Hotel0362015November4572610.00BBDEUCorporateCorporate000AA1No Deposit185.0281.00Transient-Party36.0000Check-Out2015-11-154
13Resort Hotel0362015November4572610.00BBAUTCorporateCorporate000AA1No Deposit185.0281.00Transient-Party36.0000Check-Out2015-11-153
0City Hotel002015August33130220.00BBPRTOnline TATA/TO000AB0No Deposit9.09.00Transient85.0000Check-Out2015-08-152
2City Hotel02562016October43162320.00BBDEUOnline TATA/TO000AA1No Deposit9.0333.00Transient-Party100.7500Check-Out2016-10-212
3Resort Hotel052017January121310.00BBPRTOnline TATA/TO000AA0No Deposit314.029.00Transient-Party40.4011Check-Out2017-01-062
5Resort Hotel0242015November45331020.00BBITACorporateCorporate000AA1No Deposit326.0281.00Transient48.0000Check-Out2015-11-162
6Resort Hotel0242015October442671510.00BBAUTCorporateCorporate000EG2No Deposit185.0281.00Transient-Party52.2000Check-Out2015-11-172
7Resort Hotel0272015November4562710.00BBFRACorporateCorporate000AA1No Deposit334.0281.00Transient-Party40.0000Check-Out2015-11-152